Fast and effective techniques for LWIR radiative transfer modeling: A dimension-reduction approach

Abstract

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model. The utility of this approach is investigated emulating the Mako LWIR hyperspectral sensor (Δλ ≃ 0.044μm, Δv~ ≃ 3.9 cm-1). This study employs physics-based metrics and loss functions to identify promising dimension-reduction techniques and reduce at-sensor radiance reconstruction error. The derived RT model shows an overall root mean square error (RMSE) of less than 1 K across reflective to emissive grey-body emissivity profiles.